Image compression using multiscale geometric edge models

[摘要] Edges are of particular interest for image compression, as they communicate important information, contribute large amounts of high-frequency energy, and can generally be described with few parameters. Many of today;;s most competitive coders rely on wavelets to transform and compress the image, but modeling the joint behavior of wavelet coefficients along an edge presents a distinct challenge. In this thesis, we examine techniques for exploiting the simple geometric structure which captures edge information. Using a multiscale wedgelet decomposition, we present methods for extracting and compressing a cartoon sketch containing the significant edge information, and we discuss practical issues associated with coding the residual textures. Extending these techniques, we propose a rate-distortion optimal framework (based on the Space-Frequency Quantization algorithm) using wedgelets to capture geometric information and wavelets to describe the rest. At low bitrates, this method yields compressed images with sharper edges and lower mean-square error.

[发布机构] Rice University

[效力级别] Electrical engineering